ArticleAnnals of neurology2025
Toward a Universal Map of EEG: A Semantic, Low-Dimensional Manifold for EEG Classification, Clustering, and Prognostication.
Article in Annals of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- [A method for predicting neurological outcomes after cardiac arrest based on time-frequency domain feature fusion].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Between Patterns and Predictions: Interpretable Latent EEG Representations for Clinical Insights.medRxiv : the preprint server for health sciences · 2026Article
- Prospective validation protocol for artificial intelligence-assisted standardization of Coma Recovery Scale-Revised scores using electroencephalography and clinical data in disorders of consciousness.Frontiers in medicine · 2026Article
- Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
objectivePrognostication in patients with disorders of consciousness (DOCs) remains challenging because of heterogeneous etiologies, pathophysiologies and, consequently, highly variable electroencephalograms (EEGs). Here, we use EEG patterns that are well-characterizable to create a latent map that positions novel EEGs along a continuum. We asses this map as a generalizable tool to extract prognostically valuable information from long-term EEG, by predicting outcome post-cardiac arrest as a first use case.
methodsCategorizable EEGs across the health-disease continuum (wake [W], sleep [rapid eye movement (REM), non-REM (N1, N2, N3)], ictal-interictal-continuum [lateralized and generalized periodic discharges (LPD, GPD) and lateralized and generalized rhythmic delta activity (LRDA, GRDA)], seizures [SZ], burst suppression [BS]; 20,043 patients, 288,986 EEG segments) are arranged meaningfully in a low-dimensional space via a deep neural network, resulting in a universal map of EEG (UM-EEG). We assess prognostication after cardiac arrest (576 patients, recovery or death) based on long-term EEGs represented as trajectories in this continuous embedding space.
resultsClassification of out-of-sample EEG match state-of-the-art artificial intelligence algorithms while extending it to the currently largest set of classes across the health-disease continuum (mean area under the receiver-operating-characteristic curve [AUROCs] 1-vs-all classification: W, 0.94; REM, 0.92; N1, 0.85; N2, 0.91; N3, 0.98; GRDA, 0.97; LRDA, 0.97; SZ, 0.87; GPD, 0.99; LPD, 0.97; BS, 0.94). UM-EEG enables outcome prediction after cardiac arrest with an AUROC of 0.86 and identifies interpretable factors governing prognosis such as the distance to healthy states over time.
interpretationUM-EEG presents a novel and physiologically meaningful representation to characterize brain states along the health-disease continuum. It offers new opportunities for personalized, long-term monitoring and prognostication. ANN NEUROL 2025;98:357-368.
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